Parameter estimation in stochastic differential equations

245 Pages·2008·1.5 MB·English

by  Jaya P. N. Bishwal (auth.)

Lecture Notes in Mathematics (cid:49)(cid:57)(cid:50)(cid:51)
Editors:
J.-M.Morel,Cachan
F.Takens,Groningen
B.Teissier,Paris
Jaya P.N. Bishwal
Parameter Estimation
in Stochastic Differential
Equations
(cid:65)(cid:66)(cid:67)
Author
JayaP.N.Bishwal
DepartmentofMathematicsandStatistics
UniversityofNorthCarolinaatCharlotte
376FretwellBldg.
9201UniversityCityBlvd.
CharlotteNC28223-0001
USA
e-mail:[email protected]
URL:http://www.math.uncc.edu/∼jpbishwa
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To the memory of my late grand father
and to my parents and brothers for their love and affection
Preface
I am indebted to my advisor Arup Bose from who I learned inference for
diffusion processes during my graduate studies. I have benefited a lot from
my discussions with Yacine Ait-Sahalia, Michael Sørensen, Yuri Kutoyants
and Dan Crisan. I am grateful to all of them for their advice.
Charlotte, NC
January 30, 2007 Jaya P.N. Bishwal
Contents
Basic Notations................................................XIII
1 Parametric Stochastic Differential Equations............... 1
Part I Continuous Sampling
2 RatesofWeakConvergenceofEstimatorsinHomogeneous
Diffusions.................................................. 15
2.1 Introduction ............................................ 15
2.2 Berry-Esseen Bounds for Estimators in the Ornstein-
Uhlenbeck Process....................................... 16
2.3 Rates of Convergence in the Bernstein-von Mises Theorem
for Ergodic Diffusions .................................... 26
2.4 Rates of Convergence of the Posterior Distributions
in Ergodic Diffusions..................................... 33
2.5 Berry-Esseen Bound for the Bayes Estimator................ 41
2.6 Example: Hyperbolic Diffusion Model ...................... 47
3 Large Deviations of Estimators in Homogeneous
Diffusions.................................................. 49
3.1 Introduction ............................................ 49
3.2 Model, Assumptions and Preliminaries ..................... 50
3.3 Large Deviations for the Maximum Likelihood Estimator ..... 51
3.4 Large Deviations for Bayes Estimators ..................... 57
3.5 Examples............................................... 59
4 Local Asymptotic Mixed Normality for Nonhomogeneous
Diffusions.................................................. 61
4.1 Introduction ............................................ 61
4.2 Model, Assumptions and Preliminaries ..................... 63
X Contents
4.3 Asymptotics of the Maximum Likelihood Estimator.......... 67
4.4 The Bernstein-von Mises Type Theorem and Asymptotics
of Bayes Estimators...................................... 68
4.5 Asymptotics of Maximum Probability Estimator ............ 74
4.6 Examples............................................... 75
5 Bayes and Sequential Estimation in Stochastic PDEs ...... 79
5.1 Long Time Asymptotics.................................. 79
5.1.1 Introduction ...................................... 79
5.1.2 Model, Assumptions and Preliminaries ............... 80
5.1.3 Bernstein-von Mises Theorem....................... 82
5.1.4 Asymptotics of Bayes Estimators.................... 84
5.2 Sequential Estimation.................................... 85
5.2.1 Sequential Maximum Likelihood Estimation .......... 85
5.2.2 Example ......................................... 87
5.3 Spectral Asymptotics .................................... 88
5.3.1 Introduction ...................................... 88
5.3.2 Model and Preliminaries ........................... 90
5.3.3 The Bernstein-von Mises Theorem................... 93
5.3.4 Bayes Estimation.................................. 95
5.3.5 Example: Stochastic Heat Equation.................. 96
6 Maximum Likelihood Estimation in Fractional
Diffusions.................................................. 99
6.1 Introduction ............................................ 99
6.2 Fractional Stochastic Calculus ............................100
6.3 Maximum Likelihood Estimation in Directly Observed
Fractional Diffusions .....................................109
6.4 Maximum Likelihood Estimation in Partially Observed
Fractional Diffusions .....................................113
6.5 Examples...............................................118
Part II Discrete Sampling
7 Approximate Maximum Likelihood Estimation
in Nonhomogeneous Diffusions.............................125
7.1 Introduction ............................................125
7.2 Model, Assumptions and Definitions .......................127
7.3 Accuracy of Approximations of the Itoˆ and FS Integrals......135
7.4 Accuracy of Approximations of the Log-likelihood Function...142
7.5 Accuracy of Approximations of the Maximum Likelihood
Estimate ...............................................146
7.6 Example: Chan-Karloyi-Longstaff-Sanders Model ............148
7.7 Summary of Truncated Distributions.......................155
Contents XI
8 Rates of Weak Convergence of Estimators
in the Ornstein-Uhlenbeck Process ........................159
8.1 Introduction ............................................159
8.2 Notations and Preliminaries ..............................160
8.3 Berry-Esseen Type Bounds for AMLE1.....................162
8.4 Berry-Esseen Type Bounds for AMLE2.....................173
8.5 Berry-Esseen Type Bounds for Approximate Minimum
Contrast Estimators .....................................178
8.6 Berry-Esseen Bounds for Approximate Bayes Estimators .....192
9 Local Asymptotic Normality for Discretely Observed
Homogeneous Diffusions ...................................201
9.1 Introduction ............................................201
9.2 Model, Assumptions and Preliminaries .....................202
9.3 Weak Convergence of the Approximate Likelihood Ratio
Random Fields..........................................207
9.4 Asymptotics of Approximate Estimators and Bernstein-von
Mises Type Theorems....................................221
9.5 Example: Logistic Diffusion...............................223
10 Estimating Function for Discretely Observed
Homogeneous Diffusions ...................................225
10.1 Introduction ............................................225
10.2 Rate of Consistency......................................233
10.3 Berry-Esseen Bound .....................................238
10.4 Examples...............................................240
References.....................................................245
Index..........................................................263
Basic Notations
(Ω,F,P) probability space
R real line
C complex plane
I indicator function of a set A
A
D→[P] convergence in distribution under the measure P
→P convergence in probability P
a.s. [P] almost surely under the measure P
P-a.s. almost surely under the measure P
aann ==oO((bbnn)) aabbnnnn →is b0ounded
Xn =oP(bn) Xbnn →P 0
Xn =OP(bn) Xbnn is stochasticaXlly bounded,
i.e., lim supP{| n|>A}=0
A→∞ n bn
end of a proof
A:=B A is defined by B
A=:B B is defined by A
≡ identically equal
(cid:3) absolute continuity of two measures
i.i.d. independent and identically distributed
N(a,b) normal distribution with mean a and variance b
Φ(.) standard normal distribution function
X ∼F X has the distribution F
w.r.t. with respect to
r.h.s. right hand side
l.h(cid:1).s. left hand side
a(cid:2)b maximum of a and b
a b minimum of a and b
1
Parametric Stochastic Differential Equations
Stochastic differential equations (SDEs) are a natural choice to model the
timeevolutionofdynamicsystemswhicharesubjecttorandominfluences(cf.
Arnold (1974), Van Kampen (1981)). For example, in physics the dynamics
of ions in superionic conductors are modelled via Langevin equations (cf.
Dieterichetal.(1980)),andinengineeringthedynamicsofmechanicaldevices
are described by differential equations under the influence of process noise as
errorsofmeasurement(cf.Gelb(1974)).Otherapplicationsareinbiology(cf.
Jennrich and Bright (1976)), medicine (cf. Jones (1984)), econometrics (cf.
Bergstrom (1976, 1988)), finance (cf. Black and Scholes (1973)), geophysics
(cf. Arato (1982)) and oceanography (cf. Adler et al. (1996)).
It is natural that a model contains unknown parameters. We consider the
model as the parametric Itˆo stochastic differential equation
dX = µ (θ, t, X ) dt + σ (ϑ, t, X ) dW , t≥0, X =ζ
t t t t 0
where {W ,t ≥ 0} is a standard Wiener process, µ : Θ ×[0,T]×R → R,
t
called the drift coefficient, and σ : Ξ ×[0,T]×R → R+, called the diffu-
sion coefficient, are known functions except the unknown parameters θ and
ϑ, Θ ⊂ R, Ξ ⊂ R and E(ζ2) < ∞. The drift coefficient is also called the
trend coefficient or damping coefficient or translation coefficient. The diffu-
sion coefficient is also called volatility coefficient. Under local Lipschitz and
the linear growth conditions on the coefficients µ and σ, there exists a unique
strong solution of the above Itoˆ SDE, called the diffusion process or simply a
diffusion,whichisacontinuous strongMarkov semimartingale. Thedriftand
the diffusion coefficients are respectively the instantaneous mean and instan-
taneousstandarddeviationoftheprocess.Notethatthediffusioncoefficientis
almostsurelydeterminedbytheprocess,i.e.,itcanbeestimatedwithoutany
error if observed continuously throughout a time interval (see Doob (1953),
Genon-Catalot and Jacod (1994)). We assume that the unknown parameter
in the diffusion coefficient ϑ is known and for simplicity only we shall assume
that σ =1 and our aim is to estimate the unknown parameter θ.